1. Field of the Invention
The present invention relates to artificial intelligence systems which accept a request for information or action and provide a response, which is based upon at least a partial understanding of the request which may be a natural language statement.
2. Description of the Prior Art
Artificial intelligence systems for processing a natural language statement to determine the meaning of the statement are known. These systems typically are designed as software to fully parse and categorize input language statements. Typically, the prior art natural language processing systems function in a manner analogous to the diagramming of sentences to determine the functions of the various words in the context in which they are used (noun, verb, participle, etc.). These systems determine the meaning of a statement based upon the recognition of words and the place in which they occur in the statement. A dictionary is typically used which defines each word that can be accepted, the parts of speech that each word in the dictionary can assume and the rules which govern the processing of the part of speech which the word has assumed. Once a statement has been parsed to the best of the program's ability, the program attempts to translate the statement into a computer language which can be translated into machine instructions.
The following list of publications is representative of the current state of the art of artificial intelligence systems for processing natural language statements:
Barr, A., Edward A. Feigenbaum, eds., Vols. 1 and 2, 1981; Paul R. Cohen and Edward A. Feigenbaum, eds., Vol. 3, 1982. Los Altos, CA: William Kaufmann, Inc.
Hayes-Roth, F., D. A. Waterman, D. B. Lenat, 1983, Building Expert Systems, Reading, Mass: Addison-Wesley Inc.
Koesldki, R., 1979, Logic for Problem Solving, Artificial Intelligence Series, New York: North-Holland
Minsky, M. 1963. Steps toward artificial intelligence. Computers and Thought. New York: McGraw-Hill
Winston, P. H., 1984, Artificial Intelligence, Reading, Mass: Addison-Wesley Inc.
Each of these publications presents a description of the architecture and design of natural language processor machines and also contain extensive bibliographies.
Prior art systems of the aforementioned type have several disadvantages as follows:
(1) It is assumed that the speaker uses correct syntactic and semantic forms of natural language. As a practical matter, this assumption varies from person to person.
(2) It is assumed that all possible rules for statement decomposition can be identified and catalogued in the dictionary. In fact, especially with languages such as English, there are too many exceptions to the rules to provide an all inclusive meaningful rule base.
(3) These systems require large amounts of processor operations and a large amount of storage to process each statement into the computer language. The storage requirements for the dictionary and rules base are large as a consequence of the fact that the entire supported vocabulary must be stored in the dictionary.
(4) If a statement cannot be parsed correctly, a wrong meaning will be given. There is no straightforward manner in which to communicate the misunderstanding of the input statement to the person.
(5) The language understanding process is dissociated from the machine process for determining meaning of input statements for the reason that there is typically no meaningful interaction between the user and the system.
The prior art artificial intelligence natural language processing systems have virtually no interaction between the user and the artificial intelligence system after the inputting of the statements to be analyzed. These systems provide a limited degree of interaction with the user in that they will output a statement that a word or words in the input statement are not recognized in the dictionary of words which are stored in memory. However, outside of this type of output, the prior art systems do not request additional statements to be inputted to augment the current understanding of the statement being processed. As a result, the meaning of a natural language statement is determined entirely from the statement itself which can result in erroneous meanings because of the limited amount of information conveyed by the statement.
Current knowledge bases, which are a form of artificial intelligence systems, consist of explicitly declared rules which are used to determine the response which is appropriate when a specific set of facts exists. The facts are usually gathered via a yes/no dialogue with the person. As facts are gathered, they are processed against the rules to determine the next appropriate action to be taken. These rules in prior art knowledge bases are recorded in the form of a language which generally takes the form: if (a) is true and (b) is true than (c) is a new fact or a new process to be executed. The language used to state these rules varies from an English-like programming language at one extreme to a very cryptic and difficult LISP programming language. Responses are generally stored in the knowledge base in a different form.
Current knowledge base designs have the following disadvantages: (1) It is difficult to formulate and state the rules which are used to formulate facts and to continue processing through the knowledge base. (2) Processing the rules against the current set of facts consumes a large amount of machine cycles. (3) Storage space for the rules is large. (4) The management of the knowledge base data is complex. (5) Natural language processing and response processing are separate and distinct phases of processing, increasing the system complexity and reducing the functionality of the system.